






import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf
	from tensorflow.python.eager import context
	from tensorflow.python.framework import ops

	from transformers import GradientAccumulator, create_optimizer


@require_tf
class        _snake_case    (      unittest.TestCase			):






	def  lowerCAmelCase_				(		self       ,UpperCamelCase       ,UpperCamelCase       ,UpperCamelCase							)    ->     Any:
					self.assertEqual(len(a_							)       ,len(a_							)							)
					for a, b in zip(a_       ,a_							):
									self.assertAlmostEqual(a_       ,a_       ,delta=a_							)






	def  lowerCAmelCase_				(		self							)    ->     Union[str, Any]:
					snake_case__   :Any       = GradientAccumulator()
					accumulator([tf.constant([1.0, 2.0]							)]							)
					accumulator([tf.constant([-2.0, 1.0]							)]							)
					accumulator([tf.constant([-1.0, 2.0]							)]							)
					with self.assertRaises(a_							):
									accumulator([tf.constant([1.0, 1.0]							), tf.constant([2.0, 2.0]							)]							)
					self.assertEqual(accumulator.step       ,3							)
					self.assertEqual(len(accumulator.gradients							)       ,1							)
					self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()       ,[-2.0, 5.0]       ,tol=1E-2							)
					accumulator.reset()
					self.assertEqual(accumulator.step       ,0							)
					self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()       ,[0.0, 0.0]       ,tol=1E-2							)






	def  lowerCAmelCase_				(		self							)    ->     Optional[int]:
					snake_case__   :List[str]       = None
					ops.enable_eager_execution_internal()
					snake_case__   :Any       = tf.config.list_physical_devices("CPU"							)
					if len(a_							) == 1:
									tf.config.set_logical_device_configuration(
									    physical_devices[0]       ,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]							)
					snake_case__   :Optional[int]       = tf.config.list_logical_devices(device_type="CPU"							)
					snake_case__   :Optional[Any]       = tf.distribute.MirroredStrategy(devices=devices[:2]							)

					with strategy.scope():
									snake_case__   :List[str]       = GradientAccumulator()
									snake_case__   :List[str]       = tf.Variable([4.0, 3.0]							)
									snake_case__   :Dict       = create_optimizer(5E-5       ,10       ,5							)
									snake_case__   :Optional[int]       = tf.Variable([0.0, 0.0]       ,trainable=a_							)

					def accumulate_on_replica(UpperCamelCase							):
									accumulator([gradient]							)

					def apply_on_replica():
									optimizer.apply_gradients(list(zip(accumulator.gradients       ,[variable]							)							)							)

					@tf.function
					def accumulate(UpperCamelCase       ,UpperCamelCase							):
									with strategy.scope():
													snake_case__   :int       = strategy.experimental_local_results(a_							)
													local_variables[0].assign(a_							)
													local_variables[1].assign(a_							)
													strategy.run(a_       ,args=(gradient_placeholder,)							)

					@tf.function
					def apply_grad():
									with strategy.scope():
													strategy.run(a_							)

					def _check_local_values(UpperCamelCase       ,UpperCamelCase							):
									snake_case__   :Any       = strategy.experimental_local_results(accumulator._gradients[0]							)
									self.assertListAlmostEqual(values[0].value()       ,a_       ,tol=1E-2							)
									self.assertListAlmostEqual(values[1].value()       ,a_       ,tol=1E-2							)

					accumulate([1.0, 2.0]       ,[-1.0, 1.0]							)
					accumulate([3.0, -1.0]       ,[-1.0, -1.0]							)
					accumulate([-2.0, 2.0]       ,[3.0, -2.0]							)
					self.assertEqual(accumulator.step       ,3							)
					_check_local_values([2.0, 3.0]       ,[1.0, -2.0]							)
					apply_grad()
					self.assertListAlmostEqual(variable.value()       ,[4.0, 3.0]       ,tol=1E-2							)
					accumulator.reset()
					self.assertEqual(accumulator.step       ,0							)
					_check_local_values([0.0, 0.0]       ,[0.0, 0.0]							)






'''simple docstring'''






from __future__ import annotations






def   __A    (				a_    :	float   ,a_    :	float   ,a_    :	float   ,):
		if (electron_conc, hole_conc, intrinsic_conc).count(0						) != 1:
				raise ValueError("You cannot supply more or less than 2 values"						)
		elif electron_conc < 0:
				raise ValueError("Electron concentration cannot be negative in a semiconductor"						)
		elif hole_conc < 0:
				raise ValueError("Hole concentration cannot be negative in a semiconductor"						)
		elif intrinsic_conc < 0:
				raise ValueError(
				    "Intrinsic concentration cannot be negative in a semiconductor"						)
		elif electron_conc == 0:
				return (
				    "electron_conc",
				    intrinsic_conc**2 / hole_conc,
				)
		elif hole_conc == 0:
				return (
				    "hole_conc",
				    intrinsic_conc**2 / electron_conc,
				)
		elif intrinsic_conc == 0:
				return (
				    "intrinsic_conc",
				    (electron_conc * hole_conc) ** 0.5,
				)
		else:
				return (-1, -1)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



